Technically, to respond to RQ1, we propose universal detection to expose common artifacts of fake images.
Our universal detection learns to distinguish apart fake and real images, and generalize towards open-world unseen fake images.
We adopt two different methods: image-only detection and hybrid detection.
Image-only detection shares the spirit with previous work [1]} that validates the efficacy of a simple CNN to distinguish apart GAN's fake images and real images.
In this work, we consider the most challenging and realistic scenario where we train a simple CNN on fake images generated by only one model and then evaluate it on fake images generated by many unknown models.
Hybrid detection is a brand-new detection method where text information is non-trivially incorporated into learning.
We take advantage of CLIP image and text encoders  [2]} and concatenate the two embeddings to train a real/fake binary classifier.
In the evaluation phase, if the natural texts of images are not available, we leverage BLIP [3]} to generate captions for us and then feed the images and texts to the hybrid detection.
